CMU MOCAP. PR-RRN is compared with several strong methods. As the number of frames is large, classic methods like [1]}, [2]}, [3]} fail, except CSF [4]} and CNS [5]}. CSF and CNS assume temporal smooth trajectories of points, so that the sequential frames of CMU MOCAP datasets will put these two methods in advantage. URN [6]}, C3DPO [7]}, DNRSFM [8]} and ours PR-RRN are deep models which can deal with large-scale reconstruction and do not assume temporal smoothness. Tab. REF  shows the results on the 9 subjects of CMU MOCAP. For PR-RRN, the results of unseen shapes (test set) are reported. One can see that PR-RRN outperforms all four competing methods in 9 subjects. On Subject 20, 33 and 43, PR-RRN surpasses the state-of-the-art approaches by a large margin. It is also worth noting that PR-RRN achieves high accuracy when tested with Unseen shapes on Subject 07, 38, 39 and 43. This may be from a small domain gap between the training set and the testing set. In short, the results validate the capability of PR-RRN for accurate recovery of non-rigid shapes.
